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5th International Conference on Computing, Communications, and Cyber Security, IC4S 2023

作者： Jain, Anant Gaur, Adamya Gupta, Gauranshi Mishra, Shubhangi Johari, Rahul Vidyarthi, Deo Prakash Guru Gobind Singh Indraprastha University Delhi India School of Computer and Systems Sciences Parallel and Distributed System Lab JNU Delhi India

ISBN: (纸本)9789819773701

This paper proposes a comprehensive solution to combat the growing threat of Deepfake technology, employing Convolutional Neural Networks (CNNs) and Blockchain. CNNs analyze video frames for anomalies indicative of Deepfake manipulation, while Blockchain ensures content integrity through timestamping and IPFS hashes. Leveraging transfer learning with EfficientNet-B1 architecture and dropout layers to prevent overfitting, the CNN model attains a 98.53% accuracy on testing data. Smart contracts are utilized to store social media content and AI verification results. The methodology integrates private storage on social media platforms, AI-based CNN verification, Blockchain timestamping, smart contract verification, and continuous improvement. By synergizing AI and Blockchain technologies, our approach aims to bolster defenses against deceptive content proliferation, enhancing the reliability of online media ecosystems. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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Empowering Health Insurance with Personalization: A Comprehensive Architecture Leveraging Federated Learning and BlockChain

Empowering Health Insurance with Personalization: A Comprehe...

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International Conference on Advances and Applications of Artificial Intelligence and Machine Learning, ICAAAIML 2023

作者： Jain, Anant Gupta, Gauranshi Johari, Rahul Vidyarthi, Deo Prakash Guru Gobind Singh Indraprastha University Delhi India School of Computer and Systems Sciences Parallel and Distributed System Lab JNU Delhi India

ISBN: (纸本)9789819795062

Historically, the health insurance domain has adhered to uniform premium pricing structures, often sidelining the distinct health nuances of each policyholder. Such broad-brush approaches can result in misaligned premiums, not truly reflective of individual health dynamics. Venturing into uncharted territories, this study proposes an innovative fusion of Federated Machine Learning (FML) and BlockChain methodologies to recalibrate health insurance pricing. FML, a cutting-edge evolution in the machine learning sphere, emphasizes decentralized data processing. By harnessing FML, timely insights can be gathered that mirror an individual’s health trajectory. This continuous insight generation facilitates agile adjustments to insurance premiums, ensuring they resonate with real-time health conditions. BlockChain’s decentralized ledger system acts as the bedrock of this model. Each premium modification, steered by FML-derived insights, is indelibly etched onto the BlockChain. This architecture not only masters transparency but also instills a heightened sense of trust, given the unalterable nature of BlockChain entries. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Federated learning

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An unsupervised deep learning framework for gene regulatory network inference from single-cell expression data

An unsupervised deep learning framework for gene regulatory ...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Mao, Guo Liu, Jie National University of Defense Technology Science and Technology on Parallel and Distributed Processing Laboratory Changsha410073 China National University of Defense Technology Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Laboratory of Software Engineering for Complex System Changsha410073 China

ISBN: (纸本)9798350337488

Recent advances in single-cell RNA sequencing (scRNA-seq) technology provides unprecedented opportunities for reconstruction gene regulation networks (GRNs). At present, many different models have been proposed to infer GRN from a large number of RNA-seq data, but most deep learning models use a priori gene regulatory network to infer potential GRNs. It is a challenge to reconstruct GRNs from scRNA-seq data due to the noise and sparsity introduced by the dropout effect. Here, we propose GAALink, a novel unsupervised deep learning method. It first constructs the gene similarity matrix and then refines it by threshold value. It then learns feature representations of genes through a graphical attention autoencoder that propagates information across genes with different weights. Finally, we use gene feature expression for matrix completion such that the GRNs are reconstructed. Compared with seven existing GRNs reconstruction methods, GAALink achieves more accurate performance on seven scRNA-seq dataset with four ground truth networks. GAALink can provide a useful tool for inferring GRNs for scRNA-seq expression data. © 2023 IEEE.

关键词： Feature representations Gene similarity matrix Graphical attention autoencoder Matrix completion Unsupervised deep learning method

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Adaptive and augmented active anomaly detection on dynamic network traffic streams

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Frontiers of Information Technology & Electronic Engineering 2024年第3期25卷 446-460页

作者： Bin LI Yijie WANG Li CHENG National Key Laboratory of Parallel and Distributed Computing College of ComputerNational University of Defense TechnologyChangsha 410073China College of System Engineering National University of Defense TechnologyChangsha 410073China

Active anomaly detection queries labels of sampled instances and uses them to incrementally update the detection model,and has been widely adopted in detecting network ***,existing methods cannot achieve desirable performance on dynamic network traffic streams because(1)their query strategies cannot sample informative instances to make the detection model adapt to the evolving stream and(2)their model updating relies on limited query instances only and fails to leverage the enormous unlabeled instances on *** address these issues,we propose an active tree based model,adaptive and augmented active prior-knowledge forest(A3PF),for anomaly detection on network trafic streams.A prior-knowledge forest is constructed using prior knowledge of network attacks to find feature subspaces that better distinguish network anomalies from normal *** one hand,to make the model adapt to the evolving stream,a novel adaptive query strategy is designed to sample informative instances from two aspects:the changes in dynamic data distribution and the uncertainty of *** the other hand,based on the similarity of instances in the neighborhood,we devise an augmented update method to generate pseudo labels for the unlabeled neighbors of query instances,which enables usage of the enormous unlabeled instances during model *** experiments on two benchmarks,CIC-IDS2017 and UNSW-NB15,demonstrate that A3PF achieves significant improvements over previous active methods in terms of the area under the receiver operating characteristic curve(AUC-ROC)(20.9%and 21.5%)and the area under the precision-recall curve(AUC-PR)(44.6%and 64.1%).

关键词： Active anomaly detection Network traffic streams Pseudo labels Prior knowledge of network attacks

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CRACLE: Customer Resource Allocation in CLoud Environment

CRACLE: Customer Resource Allocation in CLoud Environment

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International Conference on Data Analytics and Management, ICDAM 2022

作者： Garg, Siya Johari, Rahul Jindal, Vinita Vidyarthi, Deo Prakash Guru Gobind Singh Indraprastha University East Delhi Campus Delhi India Department of Computer Science Keshav Mahavidyalaya University of Delhi Delhi India Parallel and Distributed System Lab School of Computer and System Sciences JNU Delhi India

ISBN: (纸本)9789811976148

As well known, cloud computing follows dual pricing approach. It follows free-to-use as well as pay-per-use model approach, in which a user pays for the quantum of the resources they consume over a period of time. Depending on the demand, a customer requests for the resources and the Cloud Service Provider (CSP) allocates the resources matching to the customer’s requirements. In the past, in the simulation conducted using CloudSim software, researchers have usually worked with the pre-loaded and default values of variables for the resource allocation without considering the end-user requirement. In the current research work, a CRACLE: Customer Resource Allocation in CLoud Environment has been proposed that prompt customers for resource request and subsequent framework allocation of the same is done by the CSP. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Resource allocation

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ProMoE: Fast MoE-based LLM Serving using Proactive Caching

arXiv

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arXiv 2024年

作者： Song, Xiaoniu Zhong, Zihang Chen, Rong Chen, Haibo Institute of Parallel and Distributed Systems Shanghai Jiao Tong University China Key Laboratory of System Software Chinese Academy of Sciences China Zhejiang University China

The promising applications of large language models are often limited by the constrained GPU memory capacity available on edge devices. Mixture-of-Experts (MoE) models help address this issue by activating only a subset of the model’s parameters during computation. This approach allows the unused parameters to be offloaded to host memory, thereby reducing the overall GPU memory demand. However, existing cache-based offloading solutions handle cache misses reactively, which significantly impacts system performance. In this paper, we introduce ProMoE, a novel proactive caching system that utilizes intermediate results to predict subsequent expert usage. By proactively fetching experts in advance, ProMoE eliminates passive cache misses, removes loading time from the critical path, and reduces the performance overhead associated with offloading. Our evaluations demonstrate that ProMoE achieves an average speedup of 2.20× (up to 3.21×) and 2.07× (up to 5.02×) in the prefill and decode stages, respectively, compared to existing offloading solutions. © 2024, CC BY.

关键词： Graphics processing unit

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An unsupervised deep learning framework for gene regulatory network inference from single-cell expression data

An unsupervised deep learning framework for gene regulatory ...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Guo Mao Jie Liu Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha China Laboratory of Software Engineering for Complex System National University of Defense Technology Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha China

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Handling polysemous triggers and arguments in event extraction:an adaptive semantics learning strategy with reward-penalty mechanism

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Frontiers of Information Technology & Electronic Engineering 2025年第4期26卷 534-555页

作者： Haili LI Zhiliang TIAN Xiaodong WANG Yunyan ZHOU Shilong PAN Jie ZHOU Qiubo XU Dongsheng LI National Key Laboratory of Parallel and Distributed Computing National University of Defense TechnologyChangsha 410073China College of Computer Science and Technology National University of Defense TechnologyChangsha 410073China Unit 63891 of PLA Luoyang 471003China Unit 63893 of PLA Luoyang 471003China State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System Luoyang 471003China

Event extraction(EE)is a complex natural language processing(NLP)task that aims at identifying and classifying triggers and arguments in raw *** polysemy of triggers and arguments stands out as one of the key challenges affecting the precise extraction of *** approaches commonly consider the semantic distribution of triggers and arguments to be ***,the sample quantities of different semantics in the same trigger or argument vary in real-world scenarios,leading to a biased semantic *** bias introduces two challenges:(1)low-frequency semantics is difficult to identify;(2)high-frequency semantics is often mistakenly *** tackle these challenges,we propose an adaptive learning method with the reward-penalty mechanism for balancing the semantic distribution in polysemous triggers and *** reward-penalty mechanism balances the semantic distribution by enlarging the gap between the target and nontarget semantics by rewarding correct classifications and penalizing incorrect ***,we propose a sentencelevel event situation awareness(SA)mechanism to guide the encoder to accurately learn the knowledge of events mentioned in the sentence,thereby enhancing target event semantics in the distribution of polysemous triggers and ***,for various semantics in different tasks,we propose task-specific semantic decoders to precisely identify the boundaries of the predicted triggers and arguments for the *** experimental results on ACE2005 and its variants,along with the rich Entities,Relations,and Events(ERE),demonstrate the superiority of our approach over single-task and multi-task EE baselines.

关键词： Event extraction Polysemous triggers Polysemous arguments Semantic imbalance Reward-penalty mechanism

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Fast and scalable in-network lock management using lock fission 24

Fast and scalable in-network lock management using lock fiss...

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Proceedings of the 18th USENIX Conference on Operating systems Design and Implementation

作者： Hanze Zhang Ke Cheng Rong Chen Haibo Chen Institute of Parallel and Distributed Systems SEIEE Shanghai Jiao Tong University and Shanghai AI Laboratory and MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University Institute of Parallel and Distributed Systems SEIEE Shanghai Jiao Tong University and Engineering Research Center for Domain-specific Operating Systems Ministry of Education China Institute of Parallel and Distributed Systems SEIEE Shanghai Jiao Tong University and Shanghai AI Laboratory and Engineering Research Center for Domain-specific Operating Systems Ministry of Education China Institute of Parallel and Distributed Systems SEIEE Shanghai Jiao Tong University and Engineering Research Center for Domain-specific Operating Systems Ministry of Education China and Key Laboratory of System Software (Chinese Academy of Sciences)

ISBN: (纸本)9781939133403

distributed lock services are extensively utilized in distributed systems to serialize concurrent accesses to shared resources. The need for fast and scalable lock services has become more pronounced with decreasing task execution times and expanding dataset scales. However, traditional lock managers, reliant on server CPUs to handle lock requests, experience significant queuing delays in lock grant latency. Advanced network hardware (e.g. programmable switches) presents an avenue to manage locks without queuing delays due to their high packet processing power. Nevertheless, their constrained memory capacity restricts the manageable lock scale, thereby limiting their effect in large-scale *** paper presents FISSLOCK, a fast and scalable distributed lock service that exploits the programmable switch to improve (tail) latency and peak throughput for millions of locks. The key idea behind FISSLOCK is the concept of lock fission, which decouples lock management into grant decision and participant maintenance. FISSLOCK leverages the programmable switch to decide lock grants synchronously and relies on servers to maintain participants (i.e., holders and waiters) asynchronously. By using the programmable switch for routing, FISSLOCK enables on-demand fine-grained lock migration, thereby reducing the lock grant and release delays. FISSLOCK carefully designs and implements grant decision procedure on the programmable switch, supporting over one million locks. Evaluation using various benchmarks and a real-world application shows the efficiency of FISSLOCK. Compared to the state-of-the-art switch-based approach (NetLock), FISSLOCK cuts up to 79.1% (from 43.0%) of median lock grant time in the microbenchmark and improves transaction throughput for TATP and TPC-C by 1.76× and 2.28×, respectively.

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Securing Digital Ownership Using Non-Fungible Tokens(NFTs), an Application of BlockChain Technology 7th

Securing Digital Ownership Using Non-Fungible Tokens(NFTs), ...

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7th International Conference on Internet of Things and Connected Technologies, ICIoTCT 2022

作者： Harbola, Suhas Yadav, Jyotsna Johari, Rahul Verma, Ekta Vidyarthi, Deo Prakash Guru Gobind Singh Indraprastha University Sector-16C Delhi Dwarka India Guru Gobind Singh Indraprastha University Sector-16C Delhi Dwarka India School of Computer and Systems Sciences Parallel and Distributed System Lab JNU Delhi India National Informatics Centre New Delhi India

ISBN: (纸本)9789811997181

Non-Fungible Token(NFT) is the latest tech-jargon buzzing in IT Industry. The continuous work in the area of securing contents with BlockChain has given rise to Non-Fungible Tokens(NFTs). One of the major problems of digital assets is the claim of ownership, as to how to verify ownership of digital assets (like audios, music videos, digital art, streaming contents on the web, etc.) which is accessed/copied by billions. NFTs are unique and distinct from each other thus securing the ownership. These tokens are used to represent a digital asset and contain a proof of ownership. As the BlockChain technology grows, its applications have been designed, developed and deployed in a variety of industries. In this research, we intend to give an idea of NFT and related technology. Not only this, an effort has been made to explore Indian NFT marketplaces, and the process of selling and buying the NFTs has also been discussed. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Blockchain

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